A method for cognitive 3D geological voxel modelling of AEM data

Abstract

Airborne electromagnetic (AEM) data have proven successful for the purpose of near-surface geological mapping and are increasingly being collected worldwide. However, conversion of data from measured resistivity to lithology is not a straightforward task. Therefore, it is still challenging to make full use of these data. Many limitations must be considered before a successful geological interpretation can be performed and a reasonable 3D geological model constructed. In this paper, we propose a method for 3D geological modelling of AEM data in which the limitations are jointly considered together with a cognitive and knowledge-driven data interpretation. The modelling is performed iteratively by using voxel modelling techniques with tools developed for this exact purpose. Based on 3D resistivity grids, the tools allow the geologist to select voxel groups that define any desirable volumetric shape in the 3D model. Recent developments in octree modelling ensure exact modelling with a limited number of voxels.

Subsurface imaging of water electrical conductivity, hydraulic permeability and lithology at contaminated sites by induced polarization

“…A cognitive modeling approach 377 was followed allowing for incorporation of geological expert knowledge between observational 378 points (Royse, 2010). Furthermore, the cognitive modeling process included considerations on 379 methodological limitations of the geophysical information used translating petrophysical 380 parameters into lithological units (Jørgensen et al, 2013). With a geological setting consisting of 381 mainly undisturbed layers (Fig.…”

Geophysical, geological, and hydrogeological characterization of a tributary buried bedrock valley in southern Ontario

“…However, the majority of studies to date have focused on the use of gravity (e.g., Greenhouse and Monier-Williams 1986;Gabriel et al 2003;Gabriel 2006;Møller et al 2007;Zweirs et al 2008;Bajc and Rainsford 2010;Burt and Rainsford 2010), which exploits changes in subsurface bulk density and ground and airborne transient electromagnetic methods (e.g., Jørgensen et al 2003aJørgensen et al , 2003bJørgensen et al , 2013Sørensen and Auken 2004;Steuer et al 2009;Høyer et al 2015;Oldenborger et al 2016). While gravity methods can be highly effective in mapping changes in bedrock elevation, they provide limited insight about Quaternary infill and architecture, and are frequently accompanied by significant uncertainty in the true bedrock elevation in the absence of borehole logs.…”

Large-scale 3-D modeling by integration of resistivity models and borehole data through inversion

“…One way of building 3-D models is through a knowledge-driven (cognitive), manual approach (Jørgensen et al, 2013a). This can be carried out by making layer-cake models composed of stacked layers or by making models composed of structured or unstructured 3-D meshes where each voxel is assigned a geological/hydrogeological property.…”

“…This can be carried out by making layer-cake models composed of stacked layers or by making models composed of structured or unstructured 3-D meshes where each voxel is assigned a geological/hydrogeological property. The latter allows for a higher degree of model complexity to be incorporated (Turner, 2006;Jørgensen et al, 2013a). The cognitive approach enables various types of background knowledge such as the sedimentary processes, sequence stratigraphy, etc.…”